Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model
PaperId: :  26286
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-26286
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Mr. Y MaheshwarSri Venkatesa Perumal college of Engineering and Technology
G VyshnaviSri Venkatesa Perumal college of Engineering and Technology
K GopichandSri Venkatesa Perumal college of Engineering and Technology
T Dinesh ReddySri Venkatesa Perumal college of Engineering and Technology
T Hari PrasadSri Venkatesa Perumal college of Engineering and Technology
S NithinSri Venkatesa Perumal college of Engineering and Technology

Abstract

Sri Venkatesa Perumal college of Engineering and Technology
Keywords: Lung sound classification, Bi-ResNet, Deep learning, Respiratory disease detection, Auscultation, Time-frequency analysis, STFT, Wavelet transform, ICBHI dataset.
Respiratory diseases are leading causes of death worldwide, and failure to detect diseases at an early stage can threaten peoples lives. Previous research has pointed out that deep learning and machine learning are valid alternative strategies to detect respiratory diseases without the presence of a doctor. Thus, it is worthwhile to develop an automatic respiratory disease detection system. In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this digital system, mel-frequency cepstral coefficients (MFCCs) were used to extract the features of lung sounds, and then the K-means algorithm was used for feature clustering, to reduce the amount of data for computation. Finally, the K-nearest neighbor method was used to classify the lung sounds. The article contains an approach for removing the noise that is very difficult to filter but the removal is crucial for identifying the respiratory phases. Finally, the respiratory phases are overlaid with the frequency spectrum which simplifies the orientation in the recording and additionally offers the information on the inter- individual ratio of the inhalation and exhalation phases. Such interpretation provides a powerful tool for further analysis of lung sounds, simplify the diagnosis of various types of respiratory tract dysfunctions, and returns data which are comparable among the patients.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, and S Nithin. "Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 2 2025 Page 2537-2540
MLA Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, and S Nithin. "Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model." International Journal Of Advance Research And Innovative Ideas In Education 11.2(2025) : 2537-2540.
APA Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, & S Nithin. (2025). Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model. International Journal Of Advance Research And Innovative Ideas In Education, 11(2), 2537-2540.
Chicago Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, and S Nithin. "Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 2 (2025) : 2537-2540.
Oxford Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, and S Nithin. 'Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, 2025, p. 2537-2540. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Classification_and_Recognisation_of_Lung_Sounds_Based_on_Improved_Bi_ResNet_model_ijariie26286.pdf (Accessed : ).
Harvard Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, and S Nithin. (2025) 'Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model', International Journal Of Advance Research And Innovative Ideas In Education, 11(2), pp. 2537-2540IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Classification_and_Recognisation_of_Lung_Sounds_Based_on_Improved_Bi_ResNet_model_ijariie26286.pdf (Accessed : )
IEEE Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, and S Nithin, "Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, pp. 2537-2540, Mar-App 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Classification_and_Recognisation_of_Lung_Sounds_Based_on_Improved_Bi_ResNet_model_ijariie26286.pdf [Accessed : ].
Turabian Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, and S Nithin. "Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 2 ().
Vancouver Mr. Y Maheshwar, G Vyshnavi, K Gopichand, T Dinesh Reddy, T Hari Prasad, and S Nithin. Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet model. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(2) : 2537-2540. Available from: https://ijariie.com/AdminUploadPdf/Classification_and_Recognisation_of_Lung_Sounds_Based_on_Improved_Bi_ResNet_model_ijariie26286.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Evaluation of Machine Learning Models for Air Quality Index PredictionElectronics and Communication EngineeringI.S.Priya Download
A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16Electronics and Communication EngineeringA.Geethanjali Download
Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble LearningSri Venkatesa Perumal college of Engineering & TechnologyT.V.Ananda Babu Download
Enhanced Intrusion and Anomaly Detection in Industrial IoT Using Ensemble Learning and LSTMElecfA.Nithyavel Krishna Download
Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobileElectronics and Communication Engineering A.Geethanjali Download
Optimized Metric Learning for Enhanced Coronary Heart Disease PredictionSri Venkatesa Perumal college of Engineering and TechnologyMr. T V Ananda Babu Download
Performance Analysis of LMS-MUSIC and LMS-MVDR DOA Estimation AlgorithmELECTRONICS ENGINEERINGKAMAL NIWARIA Download
IoT SMART GARDENING SYSTEMElectronics and Communication EngineeringS. SAI PRUTHVI Download
Classification and Recognisation of Lung Sounds Based on Improved Bi-ResNet modelSri Venkatesa Perumal college of Engineering and TechnologyMr. Y Maheshwar Download
E-Ration SystemElectronics Dr. Trupti K. Wable Download
Memory & computation-aware trust worthy university e-voting solution-solidity approachSri Venkatesa Perumal College of Engineering & TechnologyK Purushotham Download
Development of a Smart Portable IoT Enabled Electric Vehicle Charging StationElectronics and Telecommunication EngineeringManisha Mohan Bagul Download
Optimized Pattern Aware Brain Tumor Segmentation Using Enhanced U-net LearningImage processingY Maheswar Download
Smart Helmet For Mining IndustryElectronics and TelecommunicationsSiddhi Vikas Ubhe Download
Train and platform accident preventionElectronics and communication engineeringSatish Malviya Download
12